High-Throughput Mass Spectrometry for Hit Identification: Current Landscape and Future Perspectives
Bibliographic record
Abstract
For nearly two decades mass spectrometry has been used as a label-free, direct-detection method for both functional and affinity-based screening of a wide range of therapeutically relevant target classes. Here, we present an overview of several established and emerging mass spectrometry platforms and summarize the unique strengths and performance characteristics of each as they apply to high-throughput screening. Multiple examples from the recent literature are highlighted in order to illustrate the power of each individual technique, with special emphasis given to cases where the use of mass spectrometry was found to be differentiating when compared with other detection formats. Indeed, as many of these examples will demonstrate, the inherent strengths of mass spectrometry-sensitivity, specificity, wide dynamic range, and amenability to complex matrices-can be leveraged to enhance the discriminating power and physiological relevance of assays included in screening cascades. It is our hope that this review will serve as a useful guide to readers of all backgrounds and experience levels on the applicability and benefits of mass spectrometry in the search for hits, leads, and, ultimately, drugs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".